Koichi Taniguchi — accepted AI-conference papers

2025

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations

Takashi Furuya, Koichi Taniguchi, Satoshi Okuda

ICLR 2025poster

Neural operators serve as universal approximators for general continuous operators. In this paper, we derive the approximation rate of solution operators for the nonlinear parabolic partial differential equations (PDEs), contributing to the quantitative approximation theorem for solution operators o…

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2022

Spectral Pruning for Recurrent Neural Networks

Takashi Furuya, Kazuma Suetake, Koichi Taniguchi, Hiroyuki Kusumoto, Ryuji Saiin, Tomohiro Daimon

AISTATS 2022poster

Recurrent neural networks (RNNs) are a class of neural networks used in sequential tasks. However, in general, RNNs have a large number of parameters and involve enormous computational costs by repeating the recurrent structures in many time steps. As a method to overcome this difficulty, RNN prunin…

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